multi-task-learning

Multi-Task Deep Neural Networks for Natural Language Understanding

🌊HMTL: Hierarchical Multi-Task Learning - A State-of-the-Art neural network model for several NLP tasks based on PyTorch and AllenNLP

TensorFlow Script

A Keras implementation of "Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts" (KDD 2018)

BERT for Multitask Learning

Awesome Multitask Learning Resources

The implementation of "End-to-End Multi-Task Learning with Attention" [CVPR 2019].

Problem Agnostic Speech Encoder

PyTorch implementation of multi-task learning architectures, incl. MTI-Net (ECCV2020).

Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)

A tensorflow implementation of MKR (Multi-task Learning for Knowledge Graph Enhanced Recommendation)

Code for "Gradient Surgery for Multi-Task Learning"

Multi-task learning smile detection, age and gender classification on GENKI4k, IMDB-Wiki dataset.

Code for Sluice networks: Learning what to share between loosely related tasks

Deep Recommenders

Rank2 solution (no-BERT) for 2019 Language and Intelligence Challenge - DuReader2.0 Machine Reading Comprehension.

a Fast, Flexible, Extensible and Easy-to-use NLP Large-scale Pretraining and Multi-task Learning Framework.

CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotations of Modality (ACL2020)

EQTransformer, a python package for earthquake signal detection and phase picking using AI.

Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, Auxiliary Tasks in Multi-task Learning

[CVPR2020] Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation, CVPR2020 (oral)

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

A Pytorch implementation of DeepCrack and RoadNet projects.

[AKBC 19] Improving Relation Extraction by Pre-trained Language Representations

Multi-Task Deep Neural Networks for Natural Language Understanding

Multi-Task Learning Framework on PyTorch. State-of-the-art methods are implemented to effectively train models on multiple tasks.

Inspired from Mask R-CNN to build a multi-task learning, two-branch architecture: one branch based on YOLOv2 for object detection, the other branch for instance segmentation. Simply tested on Rice and Shapes. MobileNet supported.

PyTorch Implementation of image quality assessment methods: IQA-CNN++ in ICIP2015 and IQA-CNN in CVPR2014

Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen, "Compacting, Picking and Growing for Unforgetting Continual Learning," Thirty-third Conference on Neural Information Processing Systems, NeurIPS 2019

[ACL 19] Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction